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[Submitted on 9 Sep 2026 (v1), last revised 11 Sep 2026 (this version, v2)]
Abstract:Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired solvers. However, translating natural-language problem descriptions into correct QUBO formulations remains difficult, requiring the identification of binary variables, constraints, objective functions, penalty terms, and suitable penalty weights. This process is time-consuming and often demands substantial domain expertise. To address this challenge, we propose an end-to-end multi-agent framework that automatically generates QUBO formulations from natural-language problem descriptions, supported by structured or unstructured test cases. To evaluate its performance, We also introduce QUBOBench, a benchmark containing 100 combinatorial optimization problems across 12 application domains, curated from peer-reviewed literature, competitions, and canonical NP-hard problems. Experimental results show that our framework achieves 68% accuracy on QUBOBench, outperforming a direct single-call baseline by 22%. Further analysis identifies iterative self-repair as the most important component contributing to improved performance. The data and code are open-sourced at this https URL.
Submission history
From: Niloy Kumar Mondal [view email]
[v1]
Wed, 9 Sep 2026 05:44:59 UTC (390 KB)
[v2]
Fri, 11 Sep 2026 02:28:17 UTC (390 KB)
추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2609.10629